Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.

Position Estimation Error Reduction Using Recursive-Least-Square Adaptive Filter for Model-Based Sensorless Interior Permanent-Magnet Synchronous Motor Drives

Abstract: To improve the performance of sensorless interior permanent-magnet synchronous motor (IPMSM) drives, an adaptive filter (AF) using recursive-least-square (RLS) algorithm is proposed for the electromotive force (EMF) model-based sliding-mode observer with a quadrature phase-locked loop (PLL) tracking estimator. The inverter nonlinearities and flux spatial harmonics, which cause the position estimation error with the sixth harmonic, are analyzed. An AF based on the adaptive noise-cancelling principle in cascade with a quadrature PLL is adopted to remove the harmonic estimation error. According to the harmonic characteristics of the estimation error from the quadrature PLL, the AF coefficients can be continuously updated by the RLS algorithm. The application of the RLS algorithm guarantees the fast convergence rate of the AF. Through the AF using the RLS algorithm, the harmonics of the estimated EMF can be effectively compensated. Therefore, the selected position estimation harmonic error can be eliminated. The effectiveness of the proposed method is verified with the experimental results at a 2.2-kW sensorless IPMSM drive.

Recursive parameter identification of the dynamical models for bilinear state space systems

Abstract: This paper investigates the recursive parameter and state estimation algorithms for a special class of nonlinear systems (i.e., bilinear state space systems). A state observer-based stochastic gradient (O-SG) algorithm is presented for the bilinear state space systems by using the gradient search. In order to improve the parameter estimation accuracy and the convergence rate of the O-SG algorithm, a state observer-based multi-innovation stochastic gradient algorithm and a state observer-based recursive least squares identification algorithm are derived by means of the multi-innovation theory. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed algorithms.

A Saddle Point Algorithm for Networked Online Convex Optimization

Abstract: An algorithm to learn optimal actions in convex distributed online problems is developed Learning is online because cost functions are revealed sequentially and distributed because they are revealed to agents of a network that can exchange information with neighboring nodes only Learning is measured in terms of the global network regret, which is defined here as the accumulated loss of causal prediction with respect to a centralized clairvoyant agent to which the information of all times and agents is revealed at the initial time A variant of the Arrow–Hurwicz saddle point algorithm is proposed to control the growth of global network regret This algorithm uses Lagrange multipliers to penalize the discrepancies between agents and leads to an implementation that relies on local operations and exchange of variables between neighbors We show that decisions made with this saddle point algorithm lead to regret whose order is not larger than $O(\sqrt{T})$ , where $T$ is the total operating time Numerical behavior is illustrated for the particular case of distributed recursive least squares An application to computer network security in which service providers cooperate to detect the signature of malicious users is developed to illustrate the practical value of the proposed algorithm

Performance analysis of the generalised projection identification for time-varying systems

Abstract: The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the time-varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.